Preprint Article Version 1 NOT YET PEER-REVIEWED

Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

  1. Departemnt of Economics and Management, North China Electric Power University, Beijing 102206, China
Version 1 : Received: 27 September 2016 / Approved: 27 September 2016 / Online: 27 September 2016 (11:16:44 CEST)

How to cite: Ma, T.; Niu, D. Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection. Preprints 2016, 2016090104 (doi: 10.20944/preprints201609.0104.v1). Ma, T.; Niu, D. Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection. Preprints 2016, 2016090104 (doi: 10.20944/preprints201609.0104.v1).

Abstract

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

Subject Areas

icing forecasting; fireworks algorithm; least square support vector machine; feature selection

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